US Open-Source AI Governance

What’s Covered?

Wilson and Hine frame the current US policy debate on open-source AI as a clash between two camps: those driven by ideological values like transparency and openness, and those focused on geopolitical concerns, especially China’s access to advanced AI. Rather than choosing a side, the authors build a joint rubric to assess policy decisions through both angles—and then apply that to four real policy ideas.

The Rubric:

Three ideological considerations:

• Transparency: open-source AI helps with external audits and understanding of model behavior.
• Progress: open models accelerate development, especially in specific-use applications.
• Power distribution: open AI decentralizes capability, empowering smaller developers and researchers.

Three geopolitical considerations:

• Misuse by China: especially through model distillation or derivative use.
• Backdoor risks: risk of hidden vulnerabilities in foreign open models, especially from China.
• Global power balance: how open-source dominance might shape tech leadership.

Key insights:

• China’s open-source ecosystem has grown more capable and less dependent on US models (especially LLaMA), though DeepSeek’s breakthroughs may still be derivative of closed US systems.
• Export controls and blunt bans risk backfiring—undermining US leadership without clearly stopping misuse.
• The debate isn’t just about China. Risks from non-state actors or domestic misuse aren’t well-covered by most proposals.
• A middle-ground “risk assessment + audit” model (like Meta’s January 2025 framework) could slow misuse without blocking innovation.

Four policies analyzed:

1. Blanket export controls – high disruption, low effectiveness, may harm US open-source leadership.
2. Model-by-model risk assessments + third-party audits – more targeted, scalable, better at balancing risk and innovation.
3. Audits for government procurement – helpful, but limited by traceability and transparency challenges.
4. Public repository of model audits – a potential public good, but effectiveness depends on available metadata and audit depth.

💡 Why it matters?

This is one of the first resources to treat ideological and national security perspectives as equally serious and worthy of structured comparison. By offering a shared vocabulary, it could shape how Congress, federal agencies, and think tanks navigate future regulatory decisions. It’s not just about what the US is willing to restrict—it’s about what kind of AI ecosystem it wants to foster, and on whose terms.

With China’s open-source models rapidly gaining traction, policy grounded in nuance—not fear or nostalgia—is going to be essential. This report helps move the conversation there.

What’s Missing?

The report is solid, but a few gaps stand out:

• Global collaboration is underexplored: There’s little attention to how the US might coordinate with allies on open-source policy, or how open-source governance might play out in multilateral forums.
• Technical thresholds need more depth: While the authors mention frontier capabilities vs. specific-use tools, they don’t provide much clarity on what technical thresholds should trigger regulation or audit.
• Practical implementation guidance: The audit-based proposals are promising, but would benefit from a deeper dive into operational issues: Who pays for the audits? How is access to model lineage enforced?

Best For:

• Congressional staff and national security agencies seeking policy tools beyond blanket bans
• Open-source AI developers navigating the policy conversation and wanting to keep models accessible
• Think tanks and researchers focused on AI governance, export controls, or US-China competition
• Civil liberties orgs monitoring how national security rhetoric may shift digital rights frameworks

Source Details:

Citation:

Claudia Wilson & Emmie Hine, US Open-Source AI Governance: Balancing Ideological and Geopolitical Considerations with China Competition, Center for AI Policy & Yale Digital Ethics Center, February 2025.

Authors:

• Claudia Wilson works with the Center for AI Policy, advising US lawmakers on AI regulation with a focus on safety and long-term governance strategy. Her work reflects ongoing collaboration with Congress and federal agencies.
• Dr. Emmie Hine is based at Yale University’s Digital Ethics Center, where she focuses on the legal and ethical implications of digital innovation. Her research spans democratic governance, human rights, and AI accountability mechanisms.

Context of publication:

This report reflects policy debates following the release of China’s DeepSeek models in late 2024 and early 2025, which raised fresh questions about openness, access, and risk. It’s meant to inform real-time policy design and complements broader regulatory moves in the US, EU, and UK.